Deep learning for downscaling tropical cyclone rainfall (Papers Track)
Emily Vosper (University of Bristol); Lucy Harris (University of Oxford); Andrew McRae (University of Oxford); Laurence Aitchison (University of Bristol); Peter Watson (Bristol); Raul Santos Rodriguez (University of Bristol); Dann Mitchell (University of Bristol)
Flooding is often the leading cause of mortality and damages from tropical cyclones. With rainfall from tropical cyclones set to rise under global warming, better estimates of extreme rainfall are required to better support resilience efforts. While high resolution climate models capture tropical cyclone statistics well, they are computationally expensive leading to a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. Here, we develop and evaluate a set of deep learning models for downscaling tropical cyclone rainfall for more robust risk analysis.